- Floyd, S., and Warmuth, M.,
**Sample compression, learnability, and the Vapnik-Chervonenkis dimension**. Machine Learning, V. 21, p. 269, 1995. Abstract from ResearchIndex. - Floyd, S. and Karp, R.,
**FFD Bin-packing for Items with Distributions on [0, 1/2]**. Algorithmica V.6, No.2, 1991, p.222-240. An earlier version of this paper appeared in**27th Annual Symposium on Foundations of Computer Science, 1986**, pp. 322-330, October 1986.

"A number of fundamental results in science, mathematics, and engineering concern critical phenomena, in which the behavior of a system depends discontinuously on some parameter. For example, the behavior of a queueing system depends critically on the ratio between the arrival rate of customers and the service rate of the server. ... In a recent paper [BLJM], a critical phenomenon is reported in the behavior of the first-fit decreasing (FFD) bin-packing algorithm when the items are drawn independently from the uniform distribution over [0, mu]. ... The present paper connects the critical phenomenon discovered in [BLJM] to the critical phenomenon mentioned above concerning queueing systems." - Floyd, S.,
**On Space-Bounded Learning and the Vapnik-Chervonenkis Dimension**. Ph.D. thesis, International Computer Science Institute Technical Report TR-89-061,**ICSI**, Berkeley CA, December 1989.

"This thesis explores algorithms that learn a concept from a concept class of Vapnik-Chervonenkis (VC) dimension d by saving at most d examples at a time. The framework is the model of probably approximately correct (pac) learning introduced by Valiant [V84]. A maximum concept class of VC dimension d is defined. For a maximum class C of VC dimension d, we give an algorithm for representing a finite set of positive and negative examples of a concept by a subset of d labeled examples of that set. This data compression scheme of size d is used to construct a space-bounded algorithm called the iterative compression algorithm that learns a concept from the class C by saving at most d examples at a time. These d examples represent the current hypothesis of the learning algorithm. A space-bounded algorithm is called acyclic if a hypothesis that has been rejected as incorrect is never reinstated. We give a sufficient condition for the iterative compression algorithm to be acyclic on a maximum class C. Classes for which the iterative compression algorithm is acyclic include positive half-spaces in Euclidean space E^n, balls in E^n, and arbitrary rectangles and triangles in the plane. The iterative compression algorithm can be thought of as learning a boundary between the positive and negative examples." - Floyd, S.,
**On Space-Bounded Learning and the Vapnik-Chervonenkis Dimension**. Proceedings of the**Second Annual Workshop on Computational Learning Theory**, Morgan Kaufmann, 1989. This is a survey of results from the thesis.

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Sally Floyd, floyd at ee.lbl.govLast modified: January, 2001